






'''simple docstring'''





import argparse
import os
import re


_UpperCamelCase						:						Optional[int]  =					'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCamelCase						:						Any  =					re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_UpperCamelCase						:						List[str]  =					re.compile(R'\s*\(\s*"(\S[^"]+)"')
def __UpperCAmelCase   (					A					:			str			,       A					:			bool = False		)    ->   Optional[Any]:
			with open(A			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Dict          =       f.read()

			UpperCAmelCase_      :       Dict          =       content.split('''\n'''		)
			UpperCAmelCase_      :       List[Any]          =       []
			UpperCAmelCase_      :       Any          =       0
			while line_idx < len(A		):
						if _re_intro_mapping.search(lines[line_idx]		) is not None:
									UpperCAmelCase_      :       Union[str, Any]          =       len(re.search(r'''^(\s*)\S'''			,       lines[line_idx]		).groups()[0]		) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(''' ''' * indent + '''('''		):
												new_lines.append(lines[line_idx]		)
												line_idx += 1

									UpperCAmelCase_      :       Any          =       []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															UpperCAmelCase_      :       List[str]          =       line_idx
															while not lines[line_idx].startswith(''' ''' * indent + ''')'''		):
																		line_idx += 1
															blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]		)		)
												else:
															blocks.append(lines[line_idx]		)
												line_idx += 1

									# Sort blocks by their identifiers
									UpperCAmelCase_      :       Dict          =       sorted(A			,       key=lambda A		: _re_identifier.search(A		).groups()[0]		)
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]		)
									line_idx += 1

			if overwrite:
						with open(A			,       '''w'''			,       encoding='''utf-8'''		) as f:
									f.write('''\n'''.join(A		)		)
			elif "\n".join(A		) != content:
						return True
def __UpperCAmelCase   (					A					:			bool = False		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Union[str, Any]          =       [os.path.join(A			,       A		) for f in os.listdir(A		) if f.endswith('''.py'''		)]
			UpperCAmelCase_      :       Optional[int]          =       [sort_auto_mapping(A			,       overwrite=A		) for fname in fnames]

			if not overwrite and any(A		):
						UpperCAmelCase_      :       int          =       [f for f, d in zip(A			,       A		) if d]
						raise ValueError(
						    F"The following files have auto mappings that need sorting: {', '.join(A		)}. Run `make style` to fix"
						    ''' this.'''		)


if __name__ == "__main__":
						_UpperCamelCase						:						Union[str, Any]  =					argparse.ArgumentParser()
						parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
						_UpperCamelCase						:						Tuple  =					parser.parse_args()

						sort_all_auto_mappings(not args.check_only)










'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  snake_case__      (					UpperCamelCase):
					a_        =					["image_processor", "tokenizer"]
					a_        =					"LayoutLMv2ImageProcessor"
					a_        =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




					def __init__(  self  :		Optional[int]				, _A  :		str=None				, _A  :		Optional[Any]=None				, **_A  :		Any      )    ->							Tuple:
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				, _A				, )
											UpperCAmelCase_      :       int          =       kwargs.pop('''feature_extractor'''      )

								UpperCAmelCase_      :       str          =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''      )
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''      )

								super().__init__(_A				, _A      )




					def __call__(  self  :		str				, _A  :		Optional[int]				, _A  :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _A  :		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				, _A  :		Union[List[List[int]], List[List[List[int]]]] = None				, _A  :		Optional[Union[List[int], List[List[int]]]] = None				, _A  :		bool = True				, _A  :		Union[bool, str, PaddingStrategy] = False				, _A  :		Union[bool, str, TruncationStrategy] = None				, _A  :		Optional[int] = None				, _A  :		int = 0				, _A  :		Optional[int] = None				, _A  :		Optional[bool] = None				, _A  :		Optional[bool] = None				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = True				, _A  :		Optional[Union[str, TensorType]] = None				, **_A  :		Dict				, )    ->							BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes '''
											    '''if you initialized the image processor with apply_ocr set to True.'''      )

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''      )

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''      )

								# first, apply the image processor
								UpperCAmelCase_      :       int          =       self.image_processor(images=_A				, return_tensors=_A      )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       int          =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
											UpperCAmelCase_      :       int          =       features['''words''']

								UpperCAmelCase_      :       str          =       self.tokenizer(
								    text=text if text is not None else features['''words''']				, text_pair=text_pair if text_pair is not None else None				, boxes=boxes if boxes is not None else features['''boxes''']				, word_labels=_A				, add_special_tokens=_A				, padding=_A				, truncation=_A				, max_length=_A				, stride=_A				, pad_to_multiple_of=_A				, return_token_type_ids=_A				, return_attention_mask=_A				, return_overflowing_tokens=_A				, return_special_tokens_mask=_A				, return_offsets_mapping=_A				, return_length=_A				, verbose=_A				, return_tensors=_A				, **_A				, )

								# add pixel values
								UpperCAmelCase_      :       int          =       features.pop('''pixel_values'''      )
								if return_overflowing_tokens is True:
											UpperCAmelCase_      :       List[Any]          =       self.get_overflowing_images(_A				, encoded_inputs['''overflow_to_sample_mapping''']      )
								UpperCAmelCase_      :       Optional[int]          =       images

								return encoded_inputs




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Tuple      )    ->							Dict:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								UpperCAmelCase_      :       Tuple          =       []
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]      )

								if len(_A      ) != len(_A      ):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F" {len(_A      )} and {len(_A      )}"      )

								return images_with_overflow




					def 		A      (  self  :		Optional[Any]				, *_A  :		Union[str, Any]				, **_A  :		Union[str, Any]      )    ->							Tuple:
								return self.tokenizer.batch_decode(*_A				, **_A      )




					def 		A      (  self  :		Any				, *_A  :		Optional[Any]				, **_A  :		Tuple      )    ->							Tuple:
								return self.tokenizer.decode(*_A				, **_A      )




					@property
					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								return ["input_ids", "bbox", "attention_mask", "image"]




					@property
					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				, _A				, )
								return self.image_processor_class





					@property
					def 		A      (  self  :		Tuple      )    ->							str:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				, _A				, )
								return self.image_processor


